Enhancing Sensitivity Classification with Semantic Features using Word Embeddings

Government documents must be reviewed to identify any sensitive information they may contain, before they can be released to the public. However, traditional paper-based sensitivity review processes are not practical for reviewing born-digital documents. Therefore, there is a timely need for automatic sensitivity classification techniques, to assist the digital sensitivity review process. However, sensitivity is typically a product of the relations between combinations of terms, such as who said what about whom, therefore, automatic sensitivity classification is a difficult task. Vector representations of terms, such as word embeddings, have been shown to be effective at encoding latent term features that preserve semantic relations between terms, which can also be beneficial to sensitivity classification. In this work, we present a thorough evaluation of the effectiveness of semantic word embedding features, along with term and grammatical features, for sensitivity classification. On a test collection of government documents containing real sensitivities, we show that extending text classification with semantic features and additional term n-grams results in significant improvements in classification effectiveness, correctly classifying 9.99% more sensitive documents compared to the text classification baseline

Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition

Cylindrical algebraic decomposition(CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over real-closed fields. When using CAD, there is often a choice for the ordering placed on the variables. This can be important, with some problems infeasible with one variable ordering but easy with another. Machine learning is the process of fitting a computer model to a complex function based on properties learned from measured data. In this paper we use machine learning (specifically a support vector machine) to select between heuristics for choosing a variable ordering, outperforming each of the separate heuristics.Comment: 16 page

Hepatitis B virus-infection related cryoglobulinemic vasculitis. Clinical manifestations and the effect of antiviral therapy: A review of the literature

Objective: Hepatitis B virus (HBV) infection causes chronic hepatitis, cirrhosis, and hepatocellular carcinoma. Furthermore, about 20% of the patients develop extrahepatic manifestations such as cryoglobulinemic vasculitis (CV), polyarteritis nodosa, non-rheumatoid arthritis, glomerulonephritis and non-Hodgkin lymphoma. This review analyzed literature data on clinical manifestations of HBV-related CV and the impact of antiviral therapy with analoques nucleotide. Methods: A PubMed search was performed to select eligible studies in the literature, up to July 2022. Results: Some studies have analyzed clinical manifestations in HBV-related CV and have investigated the role of antiviral therapy with nucleotides analogues (NAs). Clinical manifestations of CV vary from mild to moderate (purpura, asthenia and arthralgias) to severe (leg ulcers, peripheral neuropathy, glomerulonephritis, and non-Hodking lymphoma). NAs therapy leads to suppression of HBV-DNA; therefore, it is capable of producing clinical response in the majority of patients with mild to moderate symptoms. Conclusion: Antiviral therapy with NAs is the first choice for HBV suppression and control of mild to moderate disease. In severe vasculitis (glomerulonephritis, progressive peripheral neuropathy and leg ulcers), rituximab alone or with plasma-exchange is always indicated in combination with antiviral therapy

Interstitial pneumonia with autoimmune features: Why rheumatologist–pulmonologist collaboration is essential

In 2015 the European Respiratory Society (ERS) and the American Thoracic Society (ATS) “Task Force on Undifferentiated Forms of Connective Tissue Disease-associ-ated Interstitial Lung Disease” proposed classification criteria for a new research category defined as “Interstitial Pneumonia with Autoimmune Features” (IPAF), to uniformly de-fine patients with interstitial lung disease (ILD) and features of autoimmunity, without a definite connective tissue disease. These classification criteria were based on a variable combination of features obtained from three domains: a clinical domain consisting of extra-thoracic features, a serologic domain with specific autoantibodies, and a morphologic domain with imaging patterns, histopathological findings, or multicompartment in-volvement. Features suggesting a systemic vasculitis were excluded. Since publication of ERS/ATS IPAF research criteria, various retrospective studies have been published focusing on prevalence; clinical, morphological, and serological features; and prognosis of these patients showing a broad heterogeneity in the results. Recently, two prospective, cohort studies were performed, confirming the existence of some peculiarities for this clinical entity and the possible progression of IPAF to a defined connective tissue disease (CTD) in about 15% of cases. Moreover, a non-specific interstitial pneumonia pattern, an anti-nuclear antibody positivity, and a Raynaud phenomenon were the most common findings. In comparison with idiopathic pulmonary fibrosis (IPF), IPAF patients showed a better performance in pulmonary function tests and less necessity of oxygen delivery. However, at this stage of our knowledge, we believe that further prospective studies, possibly derived from multicenter cohorts and through randomized control trials, to further validate the proposed classification criteria are needed

Factors Predicting Early Failure of Etanercept in Rheumatoid Arthritis: An Analysis From the Gruppo Italiano di Studio sulla Early Arthritis (Italian Group for the Study of Early Arthritis) Registry

Objectives: This study aims to investigate the factors associated with early discontinuation (within one year) of etanercept (ETA) in rheumatoid arthritis (RA) patients who began ETA as first biologic disease-modifying antirheumatic drug (bDMARD) and who were entered into the Gruppo Italiano di Studio sulla Early Arthritis (Italian Group for the Study of Early Arthritis; GISEA) registry.Patients and methods: This registry-based cohort study included 477 RA patients (95 males, 382 females; median age 53 years; range 18 to 83 years) who began ETA as first bDMARD. Patient demographics, disease features and drugs were re-evaluated after 12 months. Baseline predictors of ETA discontinuation were estimated by univariate and multivariate analyses using Cox regression model.Results: Seventy patients (14.7%) discontinued ETA during the first year (for inefficacy in 55.8%, adverse events in 28.6%, and other reasons in 6.5%). Concurrent conventional synthetic DMARDs (csDMARDs) were reported in 54.3% of patients, mainly methotrexate (MTX), while 52.4% of subjects took low doses of glucocorticoids. Patients stopping ETA more frequently showed one or more comorbidities, mainly cardiovascular diseases (28.6% vs. 15.7% in patients stopping and continuing ETA, respectively, p=0.009). The presence of comorbidities and a combination therapy with csDMARDs other than MTX were independent factors associated with early discontinuation of ETA at multivariate Cox analysis.Conclusion: Although ETA demonstrated a high persistence in biologic-naive RA patients, about 15% of patients discontinued the treatment within 12 months. The presence of comorbidities and a combination therapy with csDMARDs other than MTX were the main factors for an early withdrawal of the drug

Fast room temperature very low field-magnetic resonance imaging system compatible with MagnetoEncephaloGraphy environment

In recent years, ultra-low field (ULF)-MRI is being given more and more attention, due to the possibility of integrating ULF-MRI and Magnetoencephalography (MEG) in the same device. Despite the signal-to-noise ratio (SNR) reduction, there are several advantages to operating at ULF, including increased tissue contrast, reduced cost and weight of the scanners, the potential to image patients that are not compatible with clinical scanners, and the opportunity to integrate different imaging modalities. The majority of ULF-MRI systems are based, until now, on magnetic field pulsed techniques for increasing SNR, using SQUID based detectors with Larmor frequencies in the kHz range. Although promising results were recently obtained with such systems, it is an open question whether similar SNR and reduced acquisition time can be achieved with simpler devices. In this work a room-temperature, MEG-compatible very-low field (VLF)-MRI device working in the range of several hundred kHz without sample pre-polarization is presented. This preserves many advantages of ULF-MRI, but for equivalent imaging conditions and SNR we achieve reduced imaging time based on preliminary results using phantoms and ex-vivo rabbits heads

Role of defects in the electronic properties of amorphous/crystalline Si interface

The mechanism determining the band alignment of the amorphous/crystalline Si heterostructures is addressed with direct atomistic simulations of the interface performed using a hierarchical combination of various computational schemes ranging from classical model-potential molecular dynamics to ab-initio methods. We found that in coordination defect-free samples the band alignment is almost vanishing and independent on interface details. In defect-rich samples, instead, the band alignment is sizeably different with respect to the defect-free case, but, remarkably, almost independent on the concentration of defects. We rationalize these findings within the theory of semiconductor interfaces.Comment: 4 pages in two-column format, 2 postscript figures include